کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
4947665 1439593 2017 28 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Correntropy-based level set method for medical image segmentation and bias correction
موضوعات مرتبط
مهندسی و علوم پایه مهندسی کامپیوتر هوش مصنوعی
پیش نمایش صفحه اول مقاله
Correntropy-based level set method for medical image segmentation and bias correction
چکیده انگلیسی
This paper presents a novel correntropy-based level set method (CLSM) for medical image segmentation and bias field correction. Firstly, we build a local bias-field-corrected fitting image (LBFI) model in the level set formulation by simultaneously using the bias field information and the local intensity information. Then, a local bias-field-corrected image fitting (LBIF) energy is introduced by minimizing the difference between the LBFI and the input image in a neighborhood, which makes it effective in segmenting images with intensity inhomogeneity. Finally, by incorporating the correntropy criterion into the LBIF energy, the proposed CLSM can decrease the weights of the samples that are away from the intensity means, which is more robust to the effects of noise. The CLSM is then integrated with respect to the neighborhood center to give a global property of image segmentation and bias field correction. Extensive experiments on both synthetic images and real medical images are provided to evaluate our method, shown significant improvements on both efficiency and accuracy, as compared with the state-of-the-art methods.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Neurocomputing - Volume 234, 19 April 2017, Pages 216-229
نویسندگان
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